Image annotation

Scope image labels around geometry and visual edge cases.

DD reviews the image set, label geometry, attribute list, and sample acceptance before proposing visual labeling.

Describe the picture type and any locale on visible text, and attach sample pictures or a link.

Scope Before quote

Requirements and feasibility checked for this request

Review Agreed method

Acceptance and ownership confirmed in writing

Dynamic Dialects reviews requests across 250+ languages. DD holds ISO 9001:2015 and ISO/IEC 27001:2022 certifications. It works with 40,000+ vetted linguists. The written scope confirms staffing, coordination, and review controls for your request.

What DD checks before quoting

Image set and rights Source images, ownership, and any text that appears in the frame.
Label geometry Boxes, polygons, attributes, scene tags, or metadata fields to apply.
Edge cases Occlusion, tiny objects, ambiguous scenes, and text-in-image rules with examples.
Output schema Coordinate format, file layout, and who accepts a sample before wider labeling.

Example scope — actual checks vary by project type and client requirements.

Illustrative DD scope worksheet

What this request needs to settle

The buyer decides geometry, attributes, and what counts as a valid object when the scene is messy. Image labeling is not free-form tagging. DD needs rights-cleared frames, box or polygon rules, attribute lists, text-in-image policy, ignore rules, and coordinate export. A sample set settles occlusion and size questions before wider visual labeling.

Illustrative visual geometry worksheet, not a client image set

Illustrative request, not a client case or a promised result.

A vision team wants store-shelf products boxed with brand attributes. A partially hidden package and tiny price tag create conflicting instructions. This hypothetical shelf scene is not a DD accuracy claim.

Inputs to check: The buyer supplies rights-cleared sample images, geometry type, attribute list, occlusion and minimum-size rules, text-in-image policy, and export schema. Sensitive store or customer imagery uses an approved transfer method.
Review decision: Annotators apply the geometry rule to hard frames and flag conflicts instead of inventing attributes. The buyer adjudicates ambiguous products and updates ignore rules. A second-pass check defines disagreement and who resolves it.
Acceptance method: Sample output could contain image ID, object ID, geometry, attributes, occlusion flag, rule version, and disposition. The buyer checks coordinates and attributes on the sample before wider work. No completeness or model gain is implied.
Exception to resolve: If the buyer changes attribute definitions mid-sample, affected classes are identified before rework. Frames the buyer cannot authorize stay outside the sample.
Record to request: Ask for a blank image label sheet with image ID, geometry fields, attributes, occlusion note, rule version, and disposition. It contains no client imagery.

Questions to settle before requesting a quote

Which geometry is required?Boxes, polygons, keypoints, or tags each need different tools and acceptance checks.
What should annotators ignore?Define occlusion, size limits, reflections, and off-shelf objects before labeling starts.
Does text inside images matter?Say whether language, script, or local wording changes the label.
Who accepts the coordinate export?Name the owner who loads the sample schema into the training or review tool.

DD checks image rights, geometry, language, and access feasibility for the request. No fixed accuracy, frame count, staffing level, or model performance is claimed. The buyer owns final visual policy and acceptance.

DD reviews the request, checks availability and requirements, and confirms the proposed scope and handling method in writing before work starts.

Picture labelling starts only after DD has examined tag meanings, example pictures, second-look needs, and tag-file shape.

Where this service fits

Image sets needing boxes, polygons, attributes, or scene tags.
Work where text inside images or local context changes the label.

What DD will confirm

A proposed visual labeling and sample-review scope.
Export schema and exception handling to agree.

Questions teams ask first

Do tiny or occluded objects need a special rule?

Yes. Share examples of occlusion, size limits, and ignore rules so annotators apply the same geometry decision across the set.

Can DD recommend a geometry type?

DD can discuss tradeoffs for the stated use; the buyer must choose the geometry and acceptance rule in writing.

What if products look alike?

Provide attribute examples and a named owner for lookalike disputes before production labeling.

Are screenshots treated like photos?

Only if the rights and label rules say so. UI chrome and capture artifacts may need their own ignore rules.